First Principles Algorithm 편집하기 (부분)

==Examples of First Principles Algorithms==
Several common first principles algorithms are used in machine learning and data science:
*'''Decision Trees''': A tree-like model that makes decisions based on features, splitting data according to conditions derived from basic rules of information gain or Gini impurity.
*'''Naïve Bayes''': A probabilistic classifier based on Bayes’ theorem, assuming independence among features. It calculates probabilities for each class and predicts the class with the highest likelihood.
*'''k-Nearest Neighbors (kNN)''': A simple algorithm that classifies a data point based on the majority label of its k closest neighbors, determined by distance measures like Euclidean distance.
*'''Linear Regression''': A statistical model that predicts outcomes by fitting a line to the data, minimizing the sum of squared residuals.
*'''Molecular Dynamics Simulations''': Uses Newtonian mechanics to predict the behavior of molecules, grounded in physics.
*'''Computational Fluid Dynamics (CFD)''': Models fluid flows based on the principles of fluid mechanics.